Use Cases
Anosys is an AI Operational Intelligence Platform. Below are practical use cases showing how teams connect AI behavior, application telemetry, infrastructure, cost, evals, user experience, and business KPIs to debug issues and improve outcomes.
Website Traffic & Performance Monitoring
Track page views, user sessions, scroll engagement, and server-side latency for any custom website — without heavyweight analytics SDKs.
| What You Can Track | How |
|---|---|
| Page views, referrers, sessions | JavaScript tag or standalone image pixel |
| Scroll depth & engagement time | Automatic via JS tracker |
| Server response time & errors | REST API from your backend |
| Custom business events | String, numeric, and boolean fields piggybacked on the URL |
With Anosys, client-side behavior and server-side events land in the same workspace, correlated by timestamp, session, URL, user, release, and custom business fields. Add anomaly detection and Slack/email routing when a page, workflow, or customer segment changes unexpectedly.
→ Read the full tutorial: How to Track Website Traffic and Performance with Anosys
AI Agent Debugging
AI agents are non-deterministic, multi-step, and expensive. A single session can consume thousands of tokens, call multiple tools, and technically succeed while the customer outcome fails. Anosys provides end-to-end tracing, evals, cost attribution, and root-cause analysis for agentic workflows.
| What You Can Track | How |
|---|---|
| Model invocations & tool calls | Native SDKs or OpenTelemetry |
| Token usage & cost attribution | Automatic per-request tracking |
| Latency breakdowns | Per-call and end-to-end timing |
| Error classification | Structured logs with auto-grouping |
| Model parameter tracking | Temperature, top-p, model version |
Supported frameworks include OpenAI Agents (Python & JavaScript), Claude Code (via the AnoSys SDK hook or OTEL), and any LLM provider via OpenTelemetry or the REST API.
→ Guides: OpenAI Agents · OpenAI ChatKit Apps · Claude Code · Anthropic Agents (OTEL) · Custom LLM Integrations
Developer Tooling Observability
Claude Code and other AI development tools are changing how teams write code, but most organizations still lack session-level evidence for cost, productivity, tool behavior, and quality.
| What You Can Track | How |
|---|---|
| Session costs & token usage | AnoSys Claude Code SDK (150+ fields per session) |
| Developer productivity metrics | Session length, tool calls, code output volume |
| Subagent & tool call tracing | Automatic with SDK hook |
| Cost attribution by project & branch | Git context extracted from transcripts |
| Content-redacted audit trails | Built-in redaction option |
| Claude Code Desktop App sessions | SSH integration for Desktop App monitoring |
The AnoSys Claude Code SDK captures session data automatically via a Stop hook — no code changes required. Combined with anomaly detection and root cause analysis, you can identify cost spikes, debug failures, and tune developer workflows with evidence.
→ Guide: Claude Code Observability
AI-to-Business Outcome Intelligence
Most AI monitoring tools only see the model layer. Most APM tools only see infrastructure. Most analytics tools only see user behavior. Anosys bridges the gap with AI operational intelligence — unified visibility across user behavior, AI agents, application services, infrastructure, cost, evals, and business outcomes.
| What Sets Anosys Apart | Why It Matters |
|---|---|
| Cross-layer correlation | Trace a user complaint through the UI, the agent's reasoning, the backend API, and the underlying infrastructure — in one view |
| User behavior tracking | Monitor how real users interact with AI features — session flows, engagement, abandonment — using a lightweight JS tag or image pixel |
| Automated anomaly detection | ML-based baselines detect silent failures, cost spikes, and quality regressions across every layer — no manual thresholds required |
| Root cause analysis | Go from "something broke" to "here's why" in minutes with causal paths that span users, agents, models, tools, applications, and infrastructure |
| Custom tracking fields | Send arbitrary string, numeric, and boolean fields to capture any business-specific signal — all automatically indexed and queryable |
Unlike point tools that focus on one layer of the stack, Anosys connects the operational chain so teams can answer why a customer outcome changed, what technical behavior caused it, and what action should happen next.
→ Read the full analysis: What Is AI Observability — And Why Current Tools Are Failing You
Network & Infrastructure Monitoring
Monitor routers, switches, firewalls, servers, and containers at scale. Anosys accepts metrics from any device that can make an HTTP call or export OpenTelemetry signals.
| What You Can Track | How |
|---|---|
| Interface utilization, packet loss, error rates | REST API or OTEL Collector |
| CPU, memory, disk I/O, network throughput | OpenTelemetry or custom agents |
| Container health & pod restarts | Kubernetes OTEL integration |
| Request rates, error rates, p95 latency | Application-level instrumentation |
Deploy an OpenTelemetry Collector as a central aggregation point, or use lightweight REST API calls from custom device agents.
→ Guide: Network & Infrastructure Observability
IoT & Edge Device Monitoring
Collect telemetry from sensors, edge gateways, and device fleets at massive scale. Track heartbeats, firmware versions, connectivity status, and environmental readings.
| What You Can Track | How |
|---|---|
| Temperature, humidity, voltage readings | REST API (HTTP GET/POST) |
| Device heartbeats & connectivity | Periodic API calls or image pixels |
| Fleet firmware versions | Custom string fields |
| Message queue depth & processing latency | OpenTelemetry or REST API |
Anosys handles millions of data points per second without sampling or data loss.
Business Process Monitoring
Instrument business workflows as observable processes. Anosys accepts arbitrary string, numeric, and boolean fields — all automatically indexed and queryable — so teams can monitor SLA health, evaluate process outcomes, find bottlenecks, and root-cause client pain points.
Examples:
- E-commerce — cart abandonment rate, checkout latency, payment success rate
- SaaS — onboarding completion, feature adoption, API error budgets, customer health scores
- Media — content engagement, ad fill rates, video completion rates
- Finance — claims workflows, transaction anomalies, SLA compliance, fraud pattern detection
Use custom pipelines, process units, and alerts to transform, enrich, evaluate, and route business process data in real time or on a batch schedule.
Getting Started
All use cases start the same way:
- Sign up at console.anosys.ai
- Create a pixel — choose the integration type that fits your use case
- Send data — follow the relevant guide above or the HTTP, OTEL, JavaScript, and pixel reference
- Explore — dashboards populate within seconds
Last updated: August 10, 2026